_Objective:_ Propose a more stable set of architectures for training GAN and show that they learn good representations of images for supervised learning and generative modeling.

_Dataset:_ [LSUN](http://www.yf.io/p/lsun) and [ImageNet 1k](www.image-net.org/).

## Architecture:

Below are the guidelines for making DCGANs.  
[![screen shot 2017-04-24 at 10 58 17 am](https://cloud.githubusercontent.com/assets/17261080/25329644/f3885f7c-28dc-11e7-8895-051124c8ff6c.png)](https://cloud.githubusercontent.com/assets/17261080/25329644/f3885f7c-28dc-11e7-8895-051124c8ff6c.png)

And here is a sample network:  
[![screen shot 2017-04-24 at 10 57 54 am](https://cloud.githubusercontent.com/assets/17261080/25329634/e9c14abc-28dc-11e7-8bed-068f7f7bc78d.png)](https://cloud.githubusercontent.com/assets/17261080/25329634/e9c14abc-28dc-11e7-8bed-068f7f7bc78d.png)

A tensorflow implementation can be found [here](https://github.com/carpedm20/DCGAN-tensorflow) along with an [online demo](https://carpedm20.github.io/faces/).

## Results:

Quite interesting especially concerning the structure learned in the Z-space and how this can be used for interpolation or object removal, see the example that is shown everywhere:  
[![screen shot 2017-04-24 at 11 20 03 am](https://cloud.githubusercontent.com/assets/17261080/25330458/080b6b4e-28e0-11e7-9ab6-ce58ef5b5562.png)](https://cloud.githubusercontent.com/assets/17261080/25330458/080b6b4e-28e0-11e7-9ab6-ce58ef5b5562.png)

Nonetheless the network is still generating small images (32x32).